# GolangUK talks notes ![](https://i.imgur.com/E4FhipS.jpg) ## About us ![](https://i.imgur.com/YVQOUU9.jpg =450x) From left to right: - Hernán Kleiman - https://twitter.com/justiniano - Sergio Moya - https://twitter.com/soyelsergillo - Gonzalo Serrano - https://twitter.com/gonzaloserrano - Ferran Orriols - https://twitter.com/ferranorriols - Alberto Fernández - https://twitter.com/albertofem #### Sponsored by Social Point - https://twitter.com/socialpointeng - http://www.socialpoint.es #### Related Ultimate Go Workshop hackpad https://hackmd.io/s/SJF9j9Wc ## Schedule http://golanguk.com/schedule ![](https://i.imgur.com/ARdhPHX.jpg =400x) ## Keynote: SOLID Go design ![](http://i.imgur.com/1d1nzZ9.jpg) > Dave Cheney - http://golanguk.com/speakers/#dave-cheney :eight: :star: :star: :star: :star: :star: :star: :star: :star: ### The **SOLID** principles Objective: design good programs. - **S**ingle Responsability - Coupling & Cohesion - package names - good - net/http, os/exec, encoding/json - importing packages increases coupling - bad: server, private, common, util - UNIX tools are small sharp tools that together solve larger tasks. Go stdlib tries to mimic that, we all shall do. - **O**pen/closed principle - open for extension - closed for modification: struct embedding example; an embedded type does'nt know its embedded; you can't change the method of the embedded type from the one that embeds it it. - **L**iskov substitution principle - Substitution powered by interfaces. Avoid concrete types if possible. - Small interfaces are more powerful (e.g `io.Reader`) - a lot of the stdlib types implement Reader - you can change one for the other and it will work - **I**nterface segregation principle - Do not force clients to implements non-required methods - Again, simpler/smaller is better. - Use the smaller interface possible that will do the work. E.g. need a `File` or you can generalize your method receiving instead a `ReadWriteCloser`? Or maybe you just call to `Write()`and you can change it for `Writer`? - Related tool https://github.com/mvdan/interfacer - **D**ependency inversion principle - structure of your import graph - should be acyclic, otherwise compile error but also it would be a design issue ### Summary - Interfaces let you apply the **SOLID** principles to Go programs. - Focus on reuse. Forget frameworks, think about design. ## Applied Go kit :six: :star: :star: :star: :star: :star: :star: > Peter Bourgon - http://golanguk.com/speakers/#peter-bourgon http://gokit.io - A toolkit for microservices ### microservices - Microservices are terrible. gokit tried to make it less terrible. 2 year old project. - Described by: - size: one dev can manage it - data: implements a single bounded context <- we like this one - ops: built and deployed independently (12Factor) - Architecture: What microservices could be?: - monolithic -> microservices - type1: rpc, CRUD-like - type2: stream-oriented, event sourcing, pipelining, queues > **Microservices solve ORGANIZATIONAL problems, but they create TECHNICAL problems.** It solves big teams org. If you have less than 5 devs you don't need them. Problems: - testing becomes really hard. - distributed transactions: another really hard problem. - deploys, monitoring, logging, distributed tracing, security... - Sean Treadway (Soundcloud) found around 40 issues with microservices ### go kit - is not a framework. It's there for help you. - similar to Twitter's Finangle (Scala) - philosophy: encourages good design - no global state - declarative #### code show - without go-kit - `Service` interface - `ServeHTTP()` to implement the `http.Handler` interface - return json - add logging before returning, could use decorators etc! - add instrumentation, use Prometheus - result: bussiness logic mixed with tons of other stuff :cry: - with go-kit - move non-bussiness stuff to middlewares - `Endpoint` func type - middlewares (using decorator design pattern) - you can also decorate your own `Service` #### transport - HTTP, you can also use it with the Gorilla mux. - gRPC https://github.com/go-kit/kit/blob/master/examples/addsvc/transport_grpc.go - Thrift, others... #### dist tracing - Zipkin (Twitter's open source distributed tracing system) - middleware to move trace ID from HTTP header to Context. ![](https://i.imgur.com/QnEYGkv.png =512x300) ## Idiomatic Go Tricks > Mat Ryer - http://matryer.com :four: :star: :star: :star: :star: - Idiomatic: - adjective: using, containing, denoting expressions. - talk natural go language - Go code usually has the same appearance - returns a tuple, {interface, error} - line of sight - quickly scan to see what it does ### tips - make happy return that last statement if possible - next time you write else, consider flipping the logic - single method interfaces - type func to implement the interface - log blocks. Prettify them adding `------` separators. - return teardown functions. teardown functions basically cleanup the called method garbage. - good timing. Basically talked about deferring stop methods on timers. Not serious business here. - use type assertion in order to execute one or another logic in your program - mocking: - simple mocks. Create a mock that implements only the methods you need from the interface you want to mock. - third party structs. If they don't provide an interface, just create it. - retrying. Do not bloat the `Try` function with arguments - empty struct implementations. - `struct{}` to group methods together - semaphores in goroutines. - buffered channels. How many concurrent goroutines you want. Special dedication to Mr **William Kennedy** who encouraged avoid use them at yesterday's workshop. - debug logs with a build tag ### Summary How to become a native speaker: - read the std lib - write obvious code - do not surprise your users - simplicity ## Cloud in your Cloud ![](http://i.imgur.com/KqPoebX.jpg =512x) > Matthew Cambell - http://golanguk.com/speakers/#matthew-campbell AKA How we build DigitalOcean :six: :star: :star: :star: :star: :star: :star: - their infra - several apps in dozen nodes - a fleet of 10K company nodes - millions of customer nodes ![](http://i.imgur.com/GrsapRr.jpg =512x) ### how to build go code - monorepo - cross-cutting concern changes in a single commit - they have custom lint tools that will open source soon to solve issues :smile: - quick security patches appliance - pull requests + code review - HTTP/JSON -> gRPC - schemas + codegen rule. Alternative http://swagger.io/specification/ - service discovery - 50% in the audience using Consul, some etcd and little ZooKeeper (he says it's a nightmare) - Consul understands regions (in fact, they have 11) for e.g if your run in a cloud env - he says SD is one if the biggest wins of Microservices arch - Consul scales for real: they have 10K nodes. - DSN SRV vs API - metrics: Prometheus cluster - they mix it with Consul to gather metrics for new services - deploy - commit to master generates - builds - tests - monorepo makes them run integration tests between microservices which code is in the same repo - docker images. - created on every build. - feature flagging - no multiple branches, no multiple versions - incremental rollout with Chef - no blue green deployments. - monitoring - they use graphite + prometheus + grafana (he encourage the use of this last one) - because prometheus is pull-oriented they don't crash a push system like they did with http://opentsdb.net/ - structured logging - ![](http://i.imgur.com/Y8B9Ksw.jpg =512x) - breaking logs down into something readable. JSON formatted log as example. - rsyslog + elasticsearch + kibana - alternatives: logdna, loggly, splunk - logging and metrics will converge - dist tracing - still sucks - global transaction ID + an ID per microservice => complicated - Zipkin => he says **it sucks** (storage and UI) Instead use opentracing. - go and do a good one in go - uptime monitoring - via Service Discovery. - Nagios in every instance. ## Advanced testing concepts for Go 1.7 ![](http://i.imgur.com/2Ybb4mP.jpg =512x) > Marcel Ban Lohuizen - Go team - http://golanguk.com/speakers/#marcel-van-lohuizen :six: :star: :star: :star: :star: :star: :star: - Go 1.7 is out (╯°. °)╯︵ ┻┻ > The testing package now supports the definition of tests with subtests and benchmarks with sub-benchmarks. This support makes it easy to write table-driven benchmarks and to create hierarchical tests. It also provides a way to share common setup and tear-down code. See the package documentation for details. -- from 1.7 release notes - tests - benchmarks - pre 1.7: table-driven tests - pros: less duplication code, less time - problem: benchmarking non-friendly, you had to create funcs and call them from the tests - 1.7: table-driven benchmarks - error messages - 1.7. They added message formatting. `t.Errorf` - error/fatal/skip - pre 1.7: first error stops the execution - 1.7: multiple errors reported with the subtest that throws it - test/benchmark isolation: how to execute just a subtest that fails - pre 1.7: you do it manually commenting etc - 1.7: - `go test --run={test_name}/"{regex}"` - every slash separates a regex. - Examples: - `go test --run=TestTime/"in Europe"` - `go test --run=TestTime/12:[0-9]` - test names are unique, if you don't pass a name it will generate a sequence - SetUp and TearDown: usually frameworks emulate XUnit - pre 1.7: ```go func TestFoo(b * testing.B) { //common set-up code // test // common tear-down code } ``` - 1.7: They encourage us to use the following approach: - in the Run func start with setup and defer the teardown. - parallelism - how to separate parallel tests that you don't want to run together? (maybe they allocate similar stuff etc) - `tc := tc` inside a concurrent loop trick :-| hope go vet detects it soon :-|++ - otherwise the loop beeing concurrent the next concurrent exec of the loop would overwrite tc and things would go nasty - teardown in parallel tests - sync parallel tests in a group putting them together inside a func runned by `r.Run()` and after that call put the teardown func ## Developing Apps for Developing Countries with go-mobile > Natalie Pistunovich - http://golanguk.com/speakers/#natalie-pistunovich :four: :star: :star: :star: :star: * experience for high consuming data applications, which are normal here but very data consuming in developing countries * this implies costs for these developing countries that people simply cannot afford ### Introduction to Africa * mercator projection: bad sizes for continents * Galls-Peters projection: Africa is actually huge * Africa is BIG * Africa population is Europe and NA combined * Africa has a 1250-3000 languages spoken, comparing with 225 spoken in Europe * data consumption of Africa is expectdly lower than Africa and NA. Very very much lower. * mobile consumption is not that much differenced * easier to bring mobile wireless network than wired connections, cable, etc. * phone types distribution shows that Africa's population mostly own featurephone (no smart features, internet, etc.) ### Initatives to improve Africa's situation * Google * Project Loon: Aerial WiFi * Project Link: Metro Fiber and WiFi * Facebook: * Aquila Aircraft * https://info.internet.org/en/ ### App Store stats * in developing countries, most of applications are social applications (Messenger, WhatsApp, Skype, Facebook) * most people don't use app store apps, they download without going through the app store * in Europe and NA most of applications are games and social ### Targeting apps for Africa * Africa is diverse as hell: no surprise which such a big habitable area * local demand: weather apps, sharing apps and educational apps. * we need to write applications who consum less data: baterry and network transfer * proposal for a solution: gomobile * multicore smartphone + Parallelism in GoMobile = Less data consumption * native vs hybrid apps * both approaches have pros and cons ### Problems when developing apps for Africa * freedom: * state-sponsor surveillance * regulation to control online media * detention and arrest of bloggers, etc. * UX * there is not much done in this are, it's still in research and much to invent by now (rules are different than Europe, NA, etc.) * symbols over words * Localization * many languages (official languages are not very adapted to reality) * Different norms ## A beginners guide to Context ![](http://i.imgur.com/rLuHhS3.jpg =512x) > Paul Crawford - http://golanguk.com/speakers/#paul-crawford :four: :star: :star: :star: :star: - problems - each new request spawns it's own goroutine - goroutines don't have any "thread local" state - you have to care about cancellation - solution: `Context` package - request scoped data - cancellation, deadlines & timeouts - it is safe for concurrent use - must be the first parameter, is a best practice > 1.7 includes context in the core standard library ### derived contexts - package provides derive new Context values from existing ones - `Background()`: the top level context for incoming request - `WithCancel()`: return a copy of the parent with a new Done channel, this is closed when the cancel function is fire - `WithTimeout()`: returns a context with the deadline - ... ### more - you can store whatever type you want in the context (we actually now that storing our type is not recommended at all) - examples: - `WithValues()`: add user session to the context - `WithTimeout()`: request to obtain the resource from an api - In 1.7: `client.Do(request.WithContext(ctx))` - `ctxthttp` helper package - context-aware HTTP requests. #### demo A search engine mixing results from DuckDuckGo and giphy ## Go from Dev to Prod > Florin Pățan - http://golanguk.com/speakers/#marcel-van-lohuizen :three: :star: :star: :star: - logging: structured with useful info - building: vendor your deps but not if you write a lib - testing: do blackbox testing using a different package name for your test (e.g instead of `demo` use `demo_test`) so you don't have access to unexported symbols from your test. - examples: use example files as documentation. - profiling: - use benchmarks - you can add benchmark results as a CI step, so if something goes slow than a threshold make the build fail - containers: use Scratch or Alpine - tracing: - client: go-kit's opentracing, x/net/trace - server: https://github.com/tracer/tracer ## Design patterns in Microservices architectures and Gilmour > Piyush Verma - http://golanguk.com/speakers/#piyush-verma https://github.com/gilmour-libs/gilmour :four: :star: :star: :star: :star: ![](https://i.imgur.com/sOHTgfK.jpg =512x380) - services vs servers - services solve a software problem and a management problem - communication design patterns - Request/Response - example with a topic layer to identify endpoints - confirmed delivery: response or error - HTTP friendly - sender handles errors - async: signals and slots - events -> queue sys -> event processors - you can scale the processors horizontally - e.g: Kafka, RabbitMQ... - patterns: fan-out, broadcast, filtering (wildcards)... - discovery and load balancing - replicated load balancer + health checking - Gilmour does Pub/Sub, there is no discovery - error - detection - timeout: server or client side - handling - ignore, publish... - composition: request piping ## static deadlock detection > Nicholas Ng - http://golanguk.com/speakers/#nicholas-ng :nine: :star: :star: :star: :star: :star: :star: :star: :star: :star: - concurrency - > don't communicate by sharing memory, share memory by communication - channels message passing - is complicated, bugs hard to find - deadlocks - `all goroutines are asleep - deadlock` - what causes a deadlock in go? 1. sender sends message, Receiver not ready 2. sender blocks goroutine and wait until Receiver ready 3. blocked goroutine goes to sleep - avoiding deadlocks - make sure sender/receiver are matching and compatible - then why are hard to avoid in practice? - go detects deadlocks at execution time and panics - "local deadlock" -> not all program is stuck - how to detect a deadlock without running the program? - static deadlock detector: github.com/nickng/dingo-hunter - models the program gorutines as FSMs - builds a graph - checks for deadlocks in the graph - modelling concurrency - learn from the masters: Hoare, Milner - *Session types* on pi-calculus ## Advanced Patterns with io.ReadWriter ![](http://i.imgur.com/PAB6Ixr.jpg =512x) > Paul Bellamy - http://golanguk.com/speakers/#paul-bellamy :eight: :star: :star: :star: :star: :star: :star: :star: :star: - What can you find in `io`, `bufio`, `ioutil`: - `Reader` - `Writer` - `Copy` - `LimitReader` - `TeeReader` - etc - composition is so easy - a stream in go is a buffer that you use to read or write in chunks ### examples - HTTP chunking. Transparently proxy a chunked HTTP in a stream. - `httputil.ChunkedReader` - problems: - strips out the chunking data - can't validate MD5 - `httputil.ChunkedReader` + `TeeReader` - prefixing, split into sections - `os.Stdout` is a `Writer` - `bufio.NewScanner(input)`, `scanner.SplitFunc`, loop over `scanner.Scan`, use `scanner.Bytes` - input is a `Reader` - `io.Pipe` syncs a `Reader` and a `Writer`, if one does not "work" then it will **block** (which is handy) ## What every developer should know about logging > Slawosz Slawinski - http://golanguk.com/speakers/#slawosz-slawinski :three: :star: :star: :star: ![](https://i.imgur.com/2WIFJuP.jpg =512x) ### logging 101 - two levels - debug: lot information - info: understand application flow, what happens in application - logging infrastructure is complex ### why do we write logs - we want to know what our application is doing - good logging is the key to solve bugs - replace lot of third party monitoring services ### logging infrastructure - is not an easy task - workflow - application is logging to stdout - stdout is redirected to file - log forwarders are moving logs to centralized log server - log server allows to browse and analyze application bahaviours - its unix style, one simple step at the time - applications - logstash - kibana - summologic - splunk ### how to structure logs in microservices - ```[timestamp][RID(request id)=foobar][RD(request delta)=0.003][LD(local delta)=0.001][service=API][search][results=432]``` - we should adapt log message structure to our application needs ### golang log libraries - package log - glog - logging library from google, allows more control on log levels ### tips and tricks - profile your logging system - log sampling: when your systems are produciong too many logs, collect only every Xth message, statiscally you will get enough logs to understand your app behaviour - security: never log user passwords, keys, name (does anyone do it?) - dapper: tracing system from google http://research.google.com/pubs/pub36356.html ## Implementing software machines in Go & C > Eleanor McHugh - http://golanguk.com/speakers/#eleanor-mchugh > :seven: :star: :star: :star: :star: :star: :star: :star: ### virtual machines * many types of virtual machines: system virtualization, hardware emulation * focus: abstract virtual machines * inspired by hardware: discrete componentes, processors, storage, communications * software machines: * stateful: state matters. With no state they don't have identity * timely: * scriptable: you need to add complex behaviour, if not, it makes no sense to have a machine in the first place * abstract virtual machine components: * stacks, CPU (registers), CPU cache, Heap, Buffers, Network and persistent store * timings in different components: depending on heap, buffer, network, etc. different magnitudes ### Memory * store data and instructions * memory model: * Von Neumann * Harvard * Forth (split model) * Hybrid * other parts (not covered) * addressing * protection * creating a heap in Go * creating sliceHeaders using slice manipulations * public interface: * `Bytes()` * `Serialise()` * `Overwrite()` * usage of unsafe: to manipulate bytes is actually legitimate * array stack: * it's a functional data structure which is actually used ### CPU * dispatch loops: * reading opcodes from mememory * change state machine internal state * switch interpreter * tokens are often single bytes * we need stack in order to implement the CPU * opcodes have mnemonics * opcodes are stored in a stack to be execute in the dispatch loop * direct call threading * each instruction is represented by a pointer to a function * instruction require a machine word * indirect threading * used to represent local jumps in the program * https://en.wikipedia.org/wiki/Threaded_code#Indirect_threading * timings: * clock pulse * synchronization: provides it across the processor ### Transport triggering * https://en.wikipedia.org/wiki/Transport_triggered_architecture * register machine architecture * exposes internal buses as components * operations are side-effects of internal writes ### Random thoughts * C VM: primitive! * distances in physical: they matter in computing, analogies with physicist * it's bad to have things not initialized by default (Rust structs must be initialized all fields by default, Golang not! You want nil pointers? Use Option) * usefulness of functional data structures * cactus stack: https://en.wikipedia.org/wiki/Parent_pointer_tree ## Grand Treatise of Modern Instrumentation and Orchestration > Björn Rabenstein - http://golanguk.com/speakers/#bjoern-rabenstein > :four: :star: :star: :star: :star: See https://prometheus.io - inspired by Google's monitoring strategy: alert for high-level stuff and allow inspecting low level. - USE method: **U**tilization, **S**aturation and **E**rror rates. Also, latency. - RED method: **R**equest count (rate) **E**rror count (rate) and **D**uration - Prometheus :moneybag: :bike: - expvar-like API and other types - you have a server and query prometheus endpoints in services - it gives you the typical go VM stats: goroutine number, memmory, GC etc - it has a query lang called PromQL - it gives you percentiles etc - counters, gauges... Counters always go up, Gauges can go down (e.g 1,2,3,4,3,4,2,1) - there are exporter tools around (e.g Apache) ## GoBridge and the Go Community: Initiatives and Opportunities > Carlisia Campos - http://golanguk.com/speakers/#carlisia-campos > :five: :star: :star: :star: :star: :star: - http://bridgefoundry.org/ - https://golangbridge.org/ - must listen https://changelog.com/gotime-6/ - they need people, you can help! - code combat https://codecombat.com/about to help people learn Go. If interested talk with William Kennedy (@goinggodotnet) ## Managing and scaling Real-time Data Pipelines using Go > Jennie Lees - http://golanguk.com/speakers/#jennie-lees > :six: :star: :star: :star: :star: :star: :star: ![](https://i.imgur.com/WBEq7IG.jpg) - works at Riot Games, better known for develop the League Of Legends (LOL) videogame. - high success: from 7 year old startup to XXX workers and millions of players - reliability -> monitoring at scale - you have to trust your monitoring systems, false alarms do the contrary - 15+ datacenters, 20K servers, 160k services, 6.5M metrics, 100K values/second, 1TB daily volume - microservices for the win - Diverse tech choices - Containers and metal - Linux, Mac and Windows - In house or as a service - 2 agents by host. - Discovery agent - Metrics agent - Endpoints by host providing data and stats in Json format. #### The pipeline ![](https://i.imgur.com/cWwb99a.jpg) #### design goals - decouple - pure interface abstraction. - no datastore details. - in order to make changes easily - resilient - batching - draining - stateless - they receive attacks - what if Kafka goes down? - HTTP native - HTTP + JSON everywhere - instrumented - Metrics are key. - fast - 75K+ values/sec on a laptop #### go - channels - they put them everywhere - visualizing them is hard - event collector pipeline: generate events, transform them, send to Kafka - design patterns - rate limiting - buffered channel - caching to avoid backpressure when readers block you - tickers with `time.After()` - pipelining - they use an empty struct channel (but should have used `context.Done()`) - `sync.WaitGroup` with `Add(1)` in every step of the pipeline and `Wait()` at the end - batching - good for client APIs - take advantage of buffers - bottleneck if ticking is very low - know your data: for metrics, client draining: if server fails, discard old items ## Building Cloud Native applications with Go > Mandy Waite - http://golanguk.com/speakers/#mandy-waite > :seven: :star: :star: :star: :star: :star: :star: :star: - cloud native evolution - build software in virtualized envs - https://cncf.io - arch: - abstractions: containers, clusters, microservices - container managers: docker, rkt - Kubernetes: - http://kubernetes.io/docs/whatisk8s - container manager + microservices - Manage applications, not machines #### The flow of Deployment ![](http://i.imgur.com/vOaB1wH.jpg =600x) #### Kubernetes features - immutability - means robustness - containers are **immutable** - various immutable server images (deployed using Spinnaker) - http://www.spinnaker.io - from [build -> package -> deploy] to [build -> package -> **construct** -> deploy] - construct the immutable graph that will be deployed - k8s is a declarative way of configuring the construct step - config - in deploy time - credentials: pod structure - env-based: k/v store - no flags or env vars to avoid restarting the app - deploy - with config / secrets - provides a general resource type system - DSL, config based on YAML - describes: load balancers, autoscaling, networking, clustering, zones, vm stuff, disk stuff... - looks like AWS API without the As a Service part - nested deployments: example with an app with a backend and a frontend sub-deployments - packages: based on https://github.com/kubernetes/helm - > Helm is a tool for managing Kubernetes charts. Charts are packages of pre-configured Kubernetes resources - CHART files (config files) <- Helm <- Repository (S3 as example) - it uses - etcd for storing and distribute cluster stats and configurations - distributed K/V, Raft-based - prometheus for monitoring which is integrated with the k8s API #### Why golang - designed for large, distributed, collaborative software projects - static compilation - strong alternative to C/C++ for cross platform simple and really functional CLI tools - low level primitives - networking - process managing - syscalls - critical mass - hipster - cool - trendy ## Building Mobile SDKs with GoMobile > Nic Jackson - http://golanguk.com/speakers/#nic-jackson :seven: :star: :star: :star: :star: :star: :star: :star: ![](https://i.imgur.com/9Nex3fp.jpg =512x) #### Creating a client SDK in Go - the horrors of generating cross platform code - c/c++: "god please no" - xamarin: better but tooling is a pain - javascript: uggh - go moblie: rather nice - go can interact with c - compile native binaries - why do we not write dry code? - we do it because dont have the time - how would we test our client? - no sandbox - binary protocol - tcp sockets #### Generationg native frameworks with Go Mobile - protobuf (rpc) - use ```protoc``` to generate the go code - create a simple rpc server - GoMobile could generate a callback to call a native client function - every in go can run native #### Integration the generated SDK into an Android app - live coding - http://github.com/gokitter <-- source code ## Seven ways to profile Go applications > Dave Cheney - http://golanguk.com/speakers/#dave-cheney :eight: :star: :star: :star: :star: :star: :star: :star: :star: 1. **time** - GNU's (`/usr/bin/time` not `time`) `/usr/bin/time -v` 2. **go build** -x + toolexec (the latter prepends the command with the tool specified) - `go build -toolexec="/usr/bin/time" cmd/compile/internal/gc` 3. **GODEBUG** env variable - stats provided by Go - `env GODEBUG=gctrace=1` 4. **Profiling** - tips - machine must be idle - watch out for power saving and thermal scaling - avoid virtual machines. too much noise. - don't use OSX < El Capitan - try to use dedicated performance test hardware - run multiple times (OS has several layers of cache and needs warmup etc) 4.1. **pprof** - descends from Google Performance Tools suite - CPU profiling - memory profiling - heap allocations (in contrast to stack allocations which are "free", heap ones are costly) - Bill tip: don't use htop & friends - block profiling - aka backpressure measurement? - similar to CPU profile but records the amount of time a goroutine spent waiting shared resources. - do not mix profiling types! - microbenchmarks - `go test -run=XXX -bench=IndexByte -cpuprofile=/tmp/c.p bytes` - XXX trick does the test just to run the benchmarks and not the tests - https://github.com/pkg/profile - good for profiling whole programs from start to end, you put the code in your program - reports can be exported to visual graphs - in the example, SVG version, the biggest box was a syscall for reading from the network. He said you can avoid to with buffering. 5. **perf** - linux tool, integrate with toolexec - what we use on osx? Maybe [this apple tool](https://developer.apple.com/library/tvos/documentation/DeveloperTools/Conceptual/InstrumentsUserGuide) can help. - looks awesome - percent on the left is how expensive the op was - framepointers, Go >=1.7 at least. otherwise doesn't look very good. 6. **flame graph** from netflix - func calls are just displayed once and grouped by size 7. **Go tool trace** - goroutines - creation/start/end - blocking/unblocking - network - system calls ## Building your own log-based message queue in Go > Victor Ruiz- http://golanguk.com/speakers/#víctor-ruiz > https://texlution.com/ :eight: :star: :star: :star: :star: :star: :star: :star: :star: Good read https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying - logs - > append only, totally-ordered sequence of records ordered by time --LinkedIn eng blog - very simple but also very fast to read and write in disk - strong ordering: not all message queues assure it but logs do - DDBBs usually write to logs first before writing to the final data structure they use - Kafka - super performant, battle tested, LinkedIn - O(1) reads and writes - see https://kafka.apache.org/08/design.html, "Constant Time Suffices" - distributed, HA, uses ZooKeeper - sometimes is too much for some cases -> **lets build one in Go** - building blocks - indexes - readers - file readers on demand implementing `io.Reader` - watcher: subscribers with channel - scanner: map in a buffered way to iterate over the log records - streamer: if you need higher throughput than the scanner - result: BigLog https://github.com/ninibe/netlog/tree/master/biglog - demo - API - simple demo with curl client against the HTTP API - pub/sub example ## Real-Time Go > Andreas Krennmair - http://golanguk.com/speakers/#andreas-krennmair :four: :star: :star: :star: :star: - GC-based langs don't have good reputation in RT systems - talk is about building a RT bidding system with Go - RT definition - something executed very fast? nop - > system... finite and specified period - aka **deadline** - types - hard: e.g patience analysis in medic systems - if deadline is missed => :bomb: - form: occasional deadline misses are tolerable although the result is not useful - soft: usefulness degrades with bigger deadline misses - e.g 3D FPS game lag (PWNT :-) - cam control systems in rooms - RT bidding - automated 2nd price auctions - 100-120ms - Nash equilibrium, game theory algos - Travel Audience (Amadeus company) - itermediary between publishers and users - they do RT bidding - segment users based on travel destinations - "The Problem" - intersect user interest and available offers - iterations - C lang, 2K QPS + memcached - never worked really well - go version - some deadlines missed - solution: set an internal deadline of 75ms - use `time.After`, also `context.CancelFunc` - storage: - first memcached - then MongoDB - https://aphyr.com/posts/322-jepsen-mongodb-stale-reads - external DDBB adds net latency - then Redis